Overall Health - Statistical Modeling

Code

library(tidyverse)
library(readr)
df <- read_csv("../data/clean_data/merged_data.csv", show_col_types = FALSE)

# Filter out rows with missing values in critical columns
df <- df %>% 
  filter(!is.na(overall_health_ranking) & 
         !is.na(coverage_access_and_affordability) &
         !is.na(health_care_quality_and_prevention) &
         !is.na(health_outcomes) &
         !is.na(medicaid_expansion_status) &
         !is.na(state_requires_coverage_of_prescription_contraception) 
         )

#head(df)

Overall Health

Overall Health Ranking by Abortion Policy

Code

# Reorder the levels of abortion policies
df$abortion_policies <- factor(df$abortion_policies, 
                               levels = c("most protective", "very protective", 
                                          "protective", "some restrictions/protections", 
                                          "restrictive", "very restrictive", "most restrictive"))

# Bin overall_health_ranking into categories
df$overall_health_category <- cut(df$overall_health_ranking,
                                  breaks = c(-Inf, 10, 20, 30, 40, 50, Inf),
                                  labels = c("most healthy", "very healthy", 
                                             "healthy", "average", 
                                             "below average", "unhealthy"))

# Stacked bar plot by abortion policy
ggplot(df, aes(x = abortion_policies, fill = overall_health_category)) +
  geom_bar(position = "fill") +
  labs(
    x = "Abortion Policy", 
    y = "Proportion", 
    fill = "Overall Health Category",
    title = "Proportion of Overall Health Categories by Abortion Policy"
  ) +
  scale_fill_manual(values = c("#1c7416", "#68bb59", "#acdf87", "#fab733", "#ff6242", "#ff0000")) + 
  theme_classic() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(size = 12, hjust = 0.5)
  )

This plot reveals that states with “most protective” abortion policies tend to have the highest proportions of “most healthy” and “very healthy” health outcomes. In contrast, as policies become more restrictive, there is an apparent increase in the proportion of states categorized as “unhealthy” or “below average.” This suggests that states with restrictive abortion policies are not only limiting access to reproductive healthcare but may also be falling behind in fostering broader public health outcomes. These restrictive states disproportionately represent environments where overall health conditions are poorer, potentially reflecting a lack of investment in comprehensive healthcare systems or preventative care infrastructure.

Alternatively, states with protective abortion policies appear to align with stronger public health systems, where access to care and preventative measures may contribute to better overall health rankings. This pattern underscores a broader connection between policy decisions prioritizing individual health rights and the overall well-being of a population.

Code

# Kruskal-Wallis Test: captures difference in overall_healths numeric rankings

kruskal_test <- kruskal.test(overall_health_ranking ~ abortion_policies, data = df)
print(kruskal_test)

    Kruskal-Wallis rank sum test

data:  overall_health_ranking by abortion_policies
Kruskal-Wallis chi-squared = 23.649, df = 6, p-value = 0.0006058

Code

# If Kruskal-Wallis is significant, run pairwise Wilcoxon tests 
pairwise.wilcox.test(df$overall_health_ranking,
                     df$abortion_policies, p.adjust.method = "bonferroni")

    Pairwise comparisons using Wilcoxon rank sum exact test 

data:  df$overall_health_ranking and df$abortion_policies 

                              most protective very protective protective
very protective               1.0000          -               -         
protective                    1.0000          1.0000          -         
some restrictions/protections 1.0000          1.0000          1.0000    
restrictive                   1.0000          1.0000          1.0000    
very restrictive              1.0000          0.8909          0.5035    
most restrictive              0.6165          0.0946          0.0034    
                              some restrictions/protections restrictive
very protective               -                             -          
protective                    -                             -          
some restrictions/protections -                             -          
restrictive                   1.0000                        -          
very restrictive              1.0000                        1.0000     
most restrictive              1.0000                        0.4437     
                              very restrictive
very protective               -               
protective                    -               
some restrictions/protections -               
restrictive                   -               
very restrictive              -               
most restrictive              1.0000          

P value adjustment method: bonferroni 

The Kruskal-Wallis Test (χ² = 23.649, df = 6, p = 0.0006058) reveals statistically significant differences in the numeric overall health rankings across the seven levels of abortion policy restrictiveness. This suggests that abortion policy restrictiveness correlates with variations in health outcomes. The low p-value (< 0.05) indicates that these differences are unlikely to have occurred by chance. This result supports the observation from the stacked bar plot: more restrictive abortion policies tend to align with poorer health outcomes, while more protective policies are associated with better health outcomes.

Code

options(repos = c(CRAN = "https://cloud.r-project.org"))
install.packages("clinfun")

The downloaded binary packages are in
    /var/folders/zl/mwx636ts1pbc006v4wqp6fwr0000gn/T//Rtmp9qiE9b/downloaded_packages

Code

library(clinfun)

# First, check sample sizes in each group
table(df$abortion_policies)

              most protective               very protective 
                            2                             7 
                   protective some restrictions/protections 
                           10                             5 
                  restrictive              very restrictive 
                            7                             4 
             most restrictive 
                           16 

Code

# Run Jonckheere-Terpstra test
jt_test <- jonckheere.test(df$overall_health_ranking, 
                          as.numeric(df$abortion_policies), 
                          alternative="increasing",
                          nperm = 10000)
print(jt_test)

    Jonckheere-Terpstra test

data:  
JT = 806.5, p-value = 1e-04
alternative hypothesis: increasing

Now we will perform linear regression to investigate the direction of this relationship. Before doing so, we must check assumptions:

Code

# Assumptions: 
library(lmtest)

Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Code

processed_data <- df %>%
  group_by(abortion_policies) %>%
  summarise(avg_ranking = mean(overall_health_ranking, na.rm = TRUE))

# 1. Linearity 
ggplot(processed_data, aes(x = factor(abortion_policies), y = avg_ranking)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE, color = "red") +
  labs(title = "Linearity Check")

`geom_smooth()` using formula = 'y ~ x'

Code

# 2. Independence 
lm_model <- lm(avg_ranking ~ as.numeric(abortion_policies), data = processed_data)
dwtest(lm_model)

    Durbin-Watson test

data:  lm_model
DW = 2.7919, p-value = 0.7472
alternative hypothesis: true autocorrelation is greater than 0

Code

# 3. Homoscedasticity
plot(lm_model, which = 1)  # plots the residuals vs fitted  

Code

bptest(lm_model) # heteroscedasticity test-- p-val < 0.05 indicates clear violation of homoscedasticity 

    studentized Breusch-Pagan test

data:  lm_model
BP = 1.3513, df = 1, p-value = 0.245

Code

#2. Normality of residuals 
plot(lm_model, which = 2)  # qq plot

Code

hist(resid(lm_model), main = "Histogram of Residuals", xlab = "Residuals")

None of the assumptions for linear regression appear to be significantly violated, based on these tests. Since the heteroscedasticity test yields. p-value of 0.245, there is no significant evidence to suggest this data violates the homoscedasticity assumption. The qq-plot and histogram indicate that the data is, for the most part, approximately Normal, but the small sample size is likely the main factor impacting the strange shape of the histogram. Nevertheless, we assume that none of these slight deviations are extreme enough to violate the assumption, so we can continue with our linear regression:

Null hypothesis: the mean overall health ranking is the same in each level of abortion policy.

Alternative hypothesis: at least one abortion policy group has a significantly different effect on the mean overall health ranking.

Code

# Linear Regression
processed_data <- df %>%
  group_by(abortion_policies) %>%
  summarise(avg_ranking = mean(overall_health_ranking, na.rm = TRUE))

# Fit linear model to calculate R-squared
lm_model <- lm(avg_ranking ~ as.numeric(abortion_policies), data = processed_data)
r_squared <- round(summary(lm_model)$r.squared, 3)

# Plot with R-squared annotation
ggplot(processed_data, aes(x = abortion_policies, y = avg_ranking)) +
  geom_point(size = 4, color = "blue") +
  geom_smooth(aes(group = 1), method = "lm", se = TRUE, color = "red") +
  labs(title = "Overall Health Ranking",
       x = "Abortion Policy",
       y = "Ranking") +
  annotate("text", 
           x = length(levels(processed_data$abortion_policies)) - 0.5, 
           y = max(processed_data$avg_ranking), 
           label = paste0("R² = ", r_squared),
           size = 5, 
           color = "black") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), 
        plot.title = element_text(hjust = 0.5, size = 14))

`geom_smooth()` using formula = 'y ~ x'

With a p-value of 0.0006981, this linear regression model provides significant evidence to reject the null hypothesis in favor of the alternative and suggest that at least one level of abortion policy has a significant inclufence on the overall health ranking. This linear regression plot shows a strong, positive relationship between abortion policy restrictiveness and overall health ranking. As abortion policies become more restrictive (moving from “most protective” to “most restrictive”), the overall health ranking worsens (increases numerically). The R² value of 0.917 indicates that 91.7% of the variation in overall health ranking is explained by the level of restrictiveness in abortion policies. This suggests a clear and significant trend where stricter abortion policies are associated with poorer overall health outcomes.

MISSING PERMUTATION TEST

Medicaid Expansion Status by Abortion Policy

Code

df$medicaid_expansion <- factor(df$medicaid_expansion_status, levels = c("0", "1"))

# Stacked bar plot of Medicaid Expansion Status with Abortion Policies as Fill
ggplot(df, aes(x = medicaid_expansion, fill = abortion_policies)) +
  geom_bar(position = "fill") +
  labs(
    x = "Medicaid Expansion Status", 
    y = "Proportion", 
    fill = "Abortion Policy",
    title = "Proportion of Abortion Policies by Medicaid Expansion Status"
  ) +
  scale_x_discrete(labels = c("0" = "Not Expanded", "1" = "Expanded")) +
  scale_fill_manual(values = c("#1c7416", "#68bb59", "#acdf87", "#fab733", "#ff6242", "#ff0000", "#c61a09")) +
  theme_classic() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(size = 12, hjust = 0.5)
  )

This plot highlights how states that have not expanded Medicaid are disproportionately associated with more restrictive abortion policies. In contrast, states that have expanded Medicaid show a higher proportion of protective and less restrictive abortion policies. This pattern suggests a potential relationship between policy choices around healthcare access (Medicaid expansion) and reproductive rights (abortion policies), where more progressive health policies align with broader healthcare access.

Code

# Fisher's Exact Test
contingency_table <- table(df$medicaid_expansion_status, df$abortion_policies)

fisher_test_result <- fisher.test(contingency_table)
print(fisher_test_result)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.03091
alternative hypothesis: two.sided

The Fisher’s Exact Test yielded a p-value of 0.03091, indicating a statistically significant association between Medicaid expansion status and abortion policies. This suggests that the distribution of Medicaid expansion (expanded vs. not expanded) varies significantly across different abortion policy categories. States’ decisions on Medicaid expansion may align with their stance on abortion policies.

Now we will perform logistic regression to investigate the direction of this relationship, since our dependent variable is a binary categorical variable:

Code

# Check assumptions for logistic regression 
table(df$medicaid_expansion_status, df$abortion_policies)

   
    most protective very protective protective some restrictions/protections
  0               0               0          0                             0
  1               2               7         10                             5
   
    restrictive very restrictive most restrictive
  0           3                1                7
  1           4                3                9

Sample sizes for logistic regression are too small, so we must use exact logistic regression to account for this size and separation:

Code

library(logistf)

# Fit the exact logistic regression model
fit <- logistf(medicaid_expansion_status ~ factor(abortion_policies), data = df)

summary(fit)

logistf(formula = medicaid_expansion_status ~ factor(abortion_policies), 
    data = df)

Model fitted by Penalized ML
Coefficients:
                                                             coef se(coef)
(Intercept)                                             1.6094379 1.549193
factor(abortion_policies)very protective                1.0986123 2.129163
factor(abortion_policies)protective                     1.4350845 2.120198
factor(abortion_policies)some restrictions/protections  0.7884574 2.140518
factor(abortion_policies)restrictive                   -1.3581235 1.705267
factor(abortion_policies)very restrictive              -0.7621401 1.830951
factor(abortion_policies)most restrictive              -1.3730491 1.624376
                                                       lower 0.95 upper 0.95
(Intercept)                                            -0.8993577   6.536132
factor(abortion_policies)very protective               -4.2449368   6.453876
factor(abortion_policies)protective                    -3.8986862   6.781282
factor(abortion_policies)some restrictions/protections -4.5677245   6.155104
factor(abortion_policies)restrictive                   -6.4096451   1.555766
factor(abortion_policies)very restrictive              -5.9125654   2.600042
factor(abortion_policies)most restrictive              -6.3586197   1.327338
                                                           Chisq         p
(Intercept)                                            1.4555158 0.2276449
factor(abortion_policies)very protective               0.2579603 0.6115249
factor(abortion_policies)protective                    0.4335919 0.5102317
factor(abortion_policies)some restrictions/protections 0.1335787 0.7147500
factor(abortion_policies)restrictive                   0.7522045 0.3857792
factor(abortion_policies)very restrictive              0.1853520 0.6668136
factor(abortion_policies)most restrictive              0.8859352 0.3465812
                                                       method
(Intercept)                                                 2
factor(abortion_policies)very protective                    2
factor(abortion_policies)protective                         2
factor(abortion_policies)some restrictions/protections      2
factor(abortion_policies)restrictive                        2
factor(abortion_policies)very restrictive                   2
factor(abortion_policies)most restrictive                   2

Method: 1-Wald, 2-Profile penalized log-likelihood, 3-None

Likelihood ratio test=10.87201 on 6 df, p=0.09241346, n=51
Wald test = 12.68851 on 6 df, p = 0.04825799

Code

# Generate predicted probabilities
df$predicted_prob <- predict(fit, type = "response")

# visualize
ggplot(df, aes(x = abortion_policies, y = predicted_prob)) +
  geom_point(position = position_jitter(width = 0.2), alpha = 0.6) +
  stat_summary(fun = mean, geom = "point", color = "red", size = 3) +
  labs(x = "Abortion Policies", 
       y = "Predicted Probability",
       title = "Medicaid Expansion Status \nPredicted Probabilities by Abortion Policy") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.title = element_text(hjust = 0.5, size = 14))

The exact logistic regression model yields a p-value of 0.0483 from the Wald test, indicating that abortion policies are significantly associated with Medicaid expansion status. The slope coefficients suggest that as abortion policies become more restrictive, the predicted probability of Medicaid expansion decreases. The visualization reinforces this relationship, showing higher predicted probabilities of Medicaid expansion among states with protective abortion policies and sharply lower probabilities in states with restrictive abortion policies. This pattern highlights a critical divide in policy decisions: states prioritizing broader reproductive rights are also more likely to expand healthcare access through Medicaid, whereas restrictive abortion policies align with reduced Medicaid expansion. This underscores the broader policy implications of prioritizing access to healthcare and reproductive rights.

Coverage, Access, and Affordability by Abortion Policy

Code

# Boxplot for coverage and abortion policies
ggplot(df, aes(x = abortion_policies, y = coverage_access_and_affordability, fill = abortion_policies)) +
  geom_boxplot() +
  scale_fill_manual(values = c("#1c7416", "#68bb59", "#acdf87", "#fab733", "#ff6242", "#ff0000", "#c61a09")) +
  labs(
    x = "Abortion Policies", 
    y = "Coverage Access and Affordability", 
    fill = "Abortion Policies",
    title = "Coverage Access and Affordability by Abortion Policies"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(size = 12, hjust = 0.5)
  )

States with “most protective” abortion policies tend to have better rankings (lower values), indicating higher coverage access and affordability. Conversely, states with more restrictive abortion policies exhibit worse rankings (higher values), suggesting reduced coverage access and affordability. This trend may reflect that states with protective abortion policies prioritize healthcare equity and access more effectively than those with restrictive policies.

This disparity also highlights inconsistencies in the priorities of restrictive states. States with protective abortion policies might integrate reproductive healthcare into broader healthcare initiatives, fostering better access and affordability. In contrast, restrictive states, which often frame their policies as being “pro-life,” may focus on generalized or inconsistent healthcare frameworks, leading to poorer outcomes. The lack of affordable and accessible healthcare in these states undermines their claims to protect life, as they fail to support the broader well-being of the populations they govern. These findings underscore both the interconnectedness between reproductive healthcare policy and overall healthcare accessibility and affordability, and the disconnect in restrictive states between policy rhetoric and actionable support for healthier communities.

Code

kruskal_test <- kruskal.test(coverage_access_and_affordability ~ abortion_policies, data = df)
print(kruskal_test)

    Kruskal-Wallis rank sum test

data:  coverage_access_and_affordability by abortion_policies
Kruskal-Wallis chi-squared = 22.583, df = 6, p-value = 0.0009489

Code

# If Kruskal-Wallis is significant, run pairwise Wilcoxon tests 
pairwise.wilcox.test(df$coverage_access_and_affordability,
                     df$abortion_policies, p.adjust.method = "bonferroni")

    Pairwise comparisons using Wilcoxon rank sum exact test 

data:  df$coverage_access_and_affordability and df$abortion_policies 

                              most protective very protective protective
very protective               1.00            -               -         
protective                    1.00            1.00            -         
some restrictions/protections 1.00            1.00            1.00      
restrictive                   1.00            1.00            0.46      
very restrictive              1.00            0.25            0.12      
most restrictive              0.27            0.12            0.03      
                              some restrictions/protections restrictive
very protective               -                             -          
protective                    -                             -          
some restrictions/protections -                             -          
restrictive                   1.00                          -          
very restrictive              1.00                          1.00       
most restrictive              1.00                          1.00       
                              very restrictive
very protective               -               
protective                    -               
some restrictions/protections -               
restrictive                   -               
very restrictive              -               
most restrictive              1.00            

P value adjustment method: bonferroni 

The Kruskal-Wallis test yielded a chi-squared value of 22.583 with 6 degrees of freedom and a p of 0.0009489. This statistically significant result indicates that there are differences in coverage access and affordability rankings among the seven abortion policy levels. States with more restrictive abortion policies appear to have higher (worse) rankings for coverage access and affordability compared to states with more protective policies, reinforcing the association between policy restrictiveness and reduced access to affordable care.

Code

# Run Jonckheere-Terpstra test

jt_test <- jonckheere.test(df$coverage_access_and_affordability, 
                          as.numeric(df$abortion_policies), 
                          alternative="increasing",
                          nperm = 10000)
print(jt_test)

    Jonckheere-Terpstra test

data:  
JT = 763.5, p-value = 1e-04
alternative hypothesis: increasing

Code

# Permutation Test Implementation for Coverage Access and Affordability
N <- 10000 

# Observed ANOVA F-statistic
observed_anova <- aov(coverage_access_and_affordability ~ abortion_policies, data = df)
observed_F <- summary(observed_anova)[[1]][["F value"]][1]

# Permutation loop
set.seed(1234) 
perm_F <- numeric(N)

for (i in 1:N) {
  # Permute the response variable
  permuted_data <- df
  permuted_data$coverage_access_and_affordability <- sample(permuted_data$coverage_access_and_affordability)
  
  # Perform ANOVA on permuted data
  perm_anova <- aov(coverage_access_and_affordability ~ abortion_policies, data = permuted_data)
  perm_F[i] <- summary(perm_anova)[[1]][["F value"]][1]
}

# Calculate p-value
p_value <- mean(perm_F >= observed_F)

cat("Observed F-statistic:", observed_F, "\n")

Observed F-statistic: 6.089754 

Code

cat("Permutation Test p-value:", p_value, "\n")

Permutation Test p-value: 1e-04 

Code

# Plot the permutation F-statistics
hist(perm_F, 
     breaks = 30, 
     main = "Permutation Distribution of F-statistic", 
     xlab = "F-statistic", 
     col = "palegreen4",  
     border = "white")    

# F-statistic line 
abline(v = observed_F, 
       col = "red",       
       lwd = 2, 
       lty = 2)

A permutation test was performed on this data in addition to the Kruskal-Wallis test to ensure the robustness of the findings across different methods. The observed F-statistic of 6.089754 (indicated by the red dashed line) lies far to the right of the permutation distribution, representing F-statistics generated under the null hypothesis. The permutation test produced a statistically significant p-value of 1x10^-4, providing strong evidence that the observed differences in coverage access and affordability across abortion policy levels are unlikely to be due to random chance. This result supports the conclusion that coverage access and affordability rankings vary significantly by abortion policy level.

Prescription Contraception Coverage Requirements by Abortion Policy

Code

df$requires_prescription <- factor(df$state_requires_coverage_of_prescription_contraception, levels = c("0", "1"))

# Stacked bar plot for Prescription Contraception Coverage Requirements
ggplot(df, aes(x = requires_prescription, fill = abortion_policies)) +
  geom_bar(position = "fill") +
  labs(x = "Requires Prescription Coverage", 
       y = "Proportion", 
       fill = "Abortion Policy",
       title = "Prescription Contraception Coverage Requirements by Abortion Policy") +
  scale_x_discrete(labels = c("0" = "Does Not Require", "1" = "Requires")) +
  scale_fill_manual(values = c("#1c7416", "#68bb59", "#acdf87", "#fab733", "#ff6242", "#ff0000", "#c61a09")) + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.title = element_text(hjust = 0, size = 12)) 

The stacked bar plot demonstrates that states that do not require prescription coverage are predominantly associated with more restrictive abortion policies, whereas states that require prescription coverage tend to align with more protective abortion policies.

This pattern reveals a significant gap in healthcare priorities among restrictive abortion policy states. By failing to require prescription coverage, particularly for contraceptives, these states not only limit access to essential tools for preventing unintended pregnancies, effectively undermining efforts to reduce the need for abortions in the first place, but also neglect the broader healthcare needs of individuals. Contraceptives are often prescribed to manage health conditions such as dysmenorrhea, endometriosis, or hormonal regulation, which can significantly impact quality of life. States with protective abortion policies, which typically require prescription coverage, demonstrate a more holistic approach to healthcare, supporting both reproductive autonomy and broader health outcomes.

Code

contingency_table <- table(df$state_requires_coverage_of_prescription_contraception, df$abortion_policies)
fisher_test_result <- fisher.test(contingency_table)
print(fisher_test_result)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.01764
alternative hypothesis: two.sided

The Fisher’s Exact Test results yield a p-value of 0.01764, indicating a statistically significant association between whether a state requires prescription coverage and its abortion policy. This suggests that the distribution of states requiring or not requiring prescription coverage is not independent of their abortion policy levels. The association highlights how policy decisions in these states may systematically limit or increase access to reproductive healthcare and broader health support systems, reinforcing disparities in healthcare infrastructure and prioritization.

Now, we will perform logistic regression to investigate the direction of this relationship:

Code

# Check assumptions for logistic regression 
table(df$state_requires_coverage_of_prescription_contraception, df$abortion_policies)

   
    most protective very protective protective some restrictions/protections
  0               0               1          1                             1
  1               2               6          9                             4
   
    restrictive very restrictive most restrictive
  0           4                2               11
  1           3                2                5

Sample sizes for logistic regression are too small, so we must use exact logistic regression to account for this size and separation…

Code

# Logistic Regression
library(logistf)

# Fit logistic regression model
fit <- logistf(state_requires_coverage_of_prescription_contraception ~ factor(abortion_policies), data = df)

# View results
summary(fit)

logistf(formula = state_requires_coverage_of_prescription_contraception ~ 
    factor(abortion_policies), data = df)

Model fitted by Penalized ML
Coefficients:
                                                             coef se(coef)
(Intercept)                                             1.6094379 1.549193
factor(abortion_policies)very protective               -0.1431008 1.794579
factor(abortion_policies)protective                     0.2363888 1.780990
factor(abortion_policies)some restrictions/protections -0.5108256 1.813529
factor(abortion_policies)restrictive                   -1.8607523 1.705267
factor(abortion_policies)very restrictive              -1.6094379 1.788854
factor(abortion_policies)most restrictive              -2.3470369 1.633638
                                                       lower 0.95 upper 0.95
(Intercept)                                            -0.8993577  6.5361319
factor(abortion_policies)very protective               -5.2583237  3.1558062
factor(abortion_policies)protective                    -4.8662731  3.5104202
factor(abortion_policies)some restrictions/protections -5.6440931  2.8216151
factor(abortion_policies)restrictive                   -6.9149344  1.0302498
factor(abortion_policies)very restrictive              -6.7355228  1.5101927
factor(abortion_policies)most restrictive              -7.3414344  0.3645081
                                                             Chisq          p
(Intercept)                                            1.455515830 0.22764495
factor(abortion_policies)very protective               0.006456591 0.93595657
factor(abortion_policies)protective                    0.017133155 0.89585942
factor(abortion_policies)some restrictions/protections 0.083322594 0.77284423
factor(abortion_policies)restrictive                   1.489808677 0.22224615
factor(abortion_policies)very restrictive              0.950172752 0.32967533
factor(abortion_policies)most restrictive              2.814056557 0.09344182
                                                       method
(Intercept)                                                 2
factor(abortion_policies)very protective                    2
factor(abortion_policies)protective                         2
factor(abortion_policies)some restrictions/protections      2
factor(abortion_policies)restrictive                        2
factor(abortion_policies)very restrictive                   2
factor(abortion_policies)most restrictive                   2

Method: 1-Wald, 2-Profile penalized log-likelihood, 3-None

Likelihood ratio test=13.07125 on 6 df, p=0.04191838, n=51
Wald test = 11.61987 on 6 df, p = 0.07100654

Code

# Generate predicted probabilities
df$predicted_prob <- predict(fit, type = "response")

# Visualize predicted probabilities
ggplot(df, aes(x = abortion_policies, y = predicted_prob)) +
  geom_point(position = position_jitter(width = 0.2), alpha = 0.6) +
  stat_summary(fun = mean, geom = "point", color = "red", size = 3) +
  labs(x = "Abortion Policies",
       y = "Predicted Probability",
       title = "Predicted Probability of Prescription Contraceptive Coverage \nby Abortion Policy") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.title = element_text(hjust = 0.5, size = 14))

The exact logistic regression analysis provides evidence of a significant relationship between abortion policy restrictiveness and the likelihood of requiring prescription contraceptive coverage, as shown by the likelihood ratio test (p=0.0419). The predicted probabilities visualized here demonstrate a clear gradient: states with more restrictive abortion policies consistently exhibit lower probabilities of requiring prescription contraceptive coverage compared to those with more protective policies.

This trend highlights a potential disconnect in restrictive states, which often emphasize policies purportedly in favor of life but fail to mandate coverage for contraceptives that could prevent unintended pregnancies or support broader healthcare needs. On the other hand, states with protective abortion policies are more likely to require coverage, aligning with healthcare frameworks that prioritize both reproductive autonomy and overall well-being. These findings underscore how policy decisions around abortion can reflect broader approaches—or lack thereof—toward supporting individuals’ health and autonomy.

Healthcare Quality and Prevention by Abortion Policy

Code

ggplot(df, aes(x = abortion_policies, y = health_care_quality_and_prevention, fill = abortion_policies)) +
  geom_boxplot() +
  scale_fill_manual(values = c("#1c7416", "#68bb59", "#acdf87", "#fab733", "#ff6242", "#ff0000", "#c61a09")) +
  labs(
    title = "Health Care Quality and Prevention by Abortion Policies",
    x = "Abortion Policies",
    y = "Health Care Quality and Prevention",
    fill = "Abortion Policies"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 12),
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "right"
  )

States with “most protective” abortion policies are clustered at the lower end of the ranking scale, indicating better healthcare quality and prevention outcomes. Conversely, states with “most restrictive” abortion policies show higher rankings, reflecting poorer performance in healthcare quality and prevention metrics. The trend suggests that states with protective abortion policies may prioritize robust healthcare systems, integrating reproductive health within broader preventative and quality-focused frameworks. In contrast, states with restrictive abortion policies consistently rank worse, potentially indicating less emphasis on preventative care or healthcare quality. This highlights how restrictive reproductive healthcare policies may correlate with broader systemic healthcare shortcomings, potentially exacerbating health inequities in those states.

Code

kruskal_test <- kruskal.test(health_care_quality_and_prevention ~ abortion_policies, data = df)
print(kruskal_test)

    Kruskal-Wallis rank sum test

data:  health_care_quality_and_prevention by abortion_policies
Kruskal-Wallis chi-squared = 14.09, df = 6, p-value = 0.02865

Code

# If Kruskal-Wallis is significant, run pairwise Wilcoxon tests 
pairwise.wilcox.test(df$health_care_quality_and_prevention,
                     df$abortion_policies, p.adjust.method = "bonferroni")

    Pairwise comparisons using Wilcoxon rank sum exact test 

data:  df$health_care_quality_and_prevention and df$abortion_policies 

                              most protective very protective protective
very protective               1.00            -               -         
protective                    1.00            1.00            -         
some restrictions/protections 1.00            1.00            1.00      
restrictive                   1.00            1.00            1.00      
very restrictive              1.00            1.00            1.00      
most restrictive              0.87            1.00            0.16      
                              some restrictions/protections restrictive
very protective               -                             -          
protective                    -                             -          
some restrictions/protections -                             -          
restrictive                   1.00                          -          
very restrictive              1.00                          1.00       
most restrictive              1.00                          0.34       
                              very restrictive
very protective               -               
protective                    -               
some restrictions/protections -               
restrictive                   -               
very restrictive              -               
most restrictive              1.00            

P value adjustment method: bonferroni 

The Kruskal-Wallis test yielded a chi-squared statistic of 22.583 (df = 6, p = 0.02865), indicating statistically significant differences in healthcare quality and prevention rankings across the seven levels of abortion policy restrictiveness. This result aligns with the observations from the boxplot, suggesting that states with more protective abortion policies tend to perform better in healthcare quality and prevention rankings, while more restrictive policies are associated with poorer outcomes.

Code

# Run Jonckheere-Terpstra test

jt_test <- jonckheere.test(df$health_care_quality_and_prevention, 
                          as.numeric(df$abortion_policies), 
                          alternative="increasing",
                          nperm = 10000)
print(jt_test)

    Jonckheere-Terpstra test

data:  
JT = 698.5, p-value = 0.0018
alternative hypothesis: increasing

Code

# Permutation Test Implementation for Health Care Quality and Prevention
N <- 10000 

# Observed ANOVA F-statistic
observed_anova <- aov(health_care_quality_and_prevention ~ abortion_policies, data = df)
observed_F <- summary(observed_anova)[[1]][["F value"]][1]

# Permutation loop
set.seed(1234) # For reproducibility
perm_F <- numeric(N)

for (i in 1:N) {
  # Permute the response variable
  permuted_data <- df
  permuted_data$health_care_quality_and_prevention <- sample(permuted_data$health_care_quality_and_prevention)
  
  # Perform ANOVA on permuted data
  perm_anova <- aov(health_care_quality_and_prevention ~ abortion_policies, data = permuted_data)
  perm_F[i] <- summary(perm_anova)[[1]][["F value"]][1]
}

# Calculate p-value
p_value <- mean(perm_F >= observed_F)

cat("Observed F-statistic:", observed_F, "\n")

Observed F-statistic: 2.812286 

Code

cat("Permutation Test p-value:", p_value, "\n")

Permutation Test p-value: 0.0186 

Code

# Plot the permutation F-statistics
hist(perm_F, 
     breaks = 30, 
     main = "Permutation Distribution of F-statistic", 
     xlab = "F-statistic", 
     col = "olivedrab4",  
     border = "white")    

# F-statistic line 
abline(v = observed_F, 
       col = "red",       
       lwd = 2, 
       lty = 2)

A permutation test was performed on the healthcare quality and prevention data in addition to the Kruskal-Wallis test to ensure the robustness of the findings across different statistical methods. The permutation test yielded a statistically significant p-value of 0.0186, confirming evidence that healthcare quality and prevention rankings differ among at least one of the abortion policy levels. This reinforces the conclusion that abortion policy restrictiveness is associated with variations in healthcare quality and prevention outcomes across states.

Code

library(dplyr)
library(ggplot2)

# Calculate the overall average
overall_avg <- mean(df$health_care_quality_and_prevention, na.rm = TRUE)

# Filter and classify observations as Above or Below Average
df <- df %>%
  filter(!is.na(abortion_policies) & !is.na(health_care_quality_and_prevention)) %>% 
  mutate(above_or_below = if_else(health_care_quality_and_prevention > overall_avg, 
                                  "Above Average", "Below Average"))

# Group and count observations
df_count <- df %>%
  group_by(abortion_policies, above_or_below) %>%
  summarize(count = n(), .groups = "drop")

# Convert counts to percentages
df_count <- df_count %>%
  group_by(abortion_policies) %>%
  mutate(percentage = count / sum(count) * 100)

# Create the stacked bar chart
ggplot(df_count, aes(x = abortion_policies, y = percentage, fill = above_or_below)) +
  geom_bar(stat = "identity", position = "stack", alpha = 0.7) +
  theme_minimal() +
  scale_fill_manual(values = c("Above Average" = "#ff0000", "Below Average" = "#1c7416")) +
  labs(x = "Abortion Policies", 
       y = "Percentage of Observations",
       fill = "Comparison to Average",
       title = "Percentage of Above/Below Average Health Care Quality and Prevention\nby Abortion Policy") +
  theme(axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
        plot.title = element_text(size = 10, hjust = 0.5),
        axis.title.y = element_text(size = 10))

Health Outcomes by Abortion Policy

Code

ggplot(df, aes(x = abortion_policies, y = health_outcomes, fill = abortion_policies)) +
  geom_boxplot() +
  scale_fill_manual(values = c("#1c7416", "#68bb59", "#acdf87", "#fab733", "#ff6242", "#ff0000", "#c61a09")) +
  labs(
    title = "Health Outcomes by Abortion Policies",
    x = "Abortion Policies",
    y = "Health Outcomes",
    fill = "Abortion Policies"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 12),
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "right"
  )

This boxplot illustrates the relationship between abortion policies and health outcomes, where a lower number/rank indicates better outcomes. States with “most protective” abortion policies demonstrate the best health outcomes, with rankings clustered toward the lower end of the scale. As abortion policies become increasingly restrictive, health outcomes generally worsen, with rankings shifting higher. States with “most restrictive” abortion policies exhibit the poorest health outcomes, with a higher concentration of rankings near the bottom of the boxplot.

These trends suggest that states with more restrictive abortion policies may lack broader investments in public health infrastructure and services, contributing to poorer overall health outcomes. In contrast, states with protective abortion policies might prioritize comprehensive healthcare systems that address a wider array of health determinants. This disparity underscores the broader implications of policy decisions on public health and well-being, highlighting potential inequities in access to essential healthcare resources.

Code

kruskal_test <- kruskal.test(health_outcomes ~ abortion_policies, data = df)
print(kruskal_test)

    Kruskal-Wallis rank sum test

data:  health_outcomes by abortion_policies
Kruskal-Wallis chi-squared = 21.669, df = 6, p-value = 0.00139

Code

# If Kruskal-Wallis is significant, run pairwise Wilcoxon tests 
pairwise.wilcox.test(df$health_outcomes,
                     df$abortion_policies, p.adjust.method = "bonferroni")

    Pairwise comparisons using Wilcoxon rank sum exact test 

data:  df$health_outcomes and df$abortion_policies 

                              most protective very protective protective
very protective               1.000           -               -         
protective                    1.000           1.000           -         
some restrictions/protections 1.000           1.000           1.000     
restrictive                   1.000           1.000           1.000     
very restrictive              1.000           1.000           1.000     
most restrictive              1.000           0.076           0.014     
                              some restrictions/protections restrictive
very protective               -                             -          
protective                    -                             -          
some restrictions/protections -                             -          
restrictive                   1.000                         -          
very restrictive              1.000                         1.000      
most restrictive              0.485                         0.211      
                              very restrictive
very protective               -               
protective                    -               
some restrictions/protections -               
restrictive                   -               
very restrictive              -               
most restrictive              1.000           

P value adjustment method: bonferroni 

The Kruskal-Wallis test for health outcomes across abortion policy levels reveals a chi-squared value of 21.669 with 6 degrees of freedom and a p-value of 0.00139. This statistically significant result indicates that health outcomes vary meaningfully across at least one level of abortion policy. This reinforces the trends observed in the boxplot: states with “most protective” abortion policies tend to achieve better health outcomes, while those with “most restrictive” policies are associated with worse rankings.

Code

#Jonckheere test

jt_test <- jonckheere.test(df$health_outcomes, 
                          as.numeric(df$abortion_policies), 
                          alternative="increasing",
                          nperm = 10000)
print(jt_test)

    Jonckheere-Terpstra test

data:  
JT = 808.5, p-value = 1e-04
alternative hypothesis: increasing

Code

# Permutation Test Implementation for Health Outcomes by Abortion Policy
N <- 10000 

# Observed ANOVA F-statistic
observed_anova <- aov(health_outcomes ~ abortion_policies, data = df)
observed_F <- summary(observed_anova)[[1]][["F value"]][1]

# Permutation loop
set.seed(1234) # For reproducibility
perm_F <- numeric(N)

for (i in 1:N) {
  # Permute the response variable
  permuted_data <- df
  permuted_data$health_outcomes <- sample(permuted_data$health_outcomes)
  
  # Perform ANOVA on permuted data
  perm_anova <- aov(health_outcomes ~ abortion_policies, data = permuted_data)
  perm_F[i] <- summary(perm_anova)[[1]][["F value"]][1]
}

# Calculate p-value
p_value <- mean(perm_F >= observed_F)

cat("Observed F-statistic:", observed_F, "\n")

Observed F-statistic: 5.608316 

Code

cat("Permutation Test p-value:", p_value, "\n")

Permutation Test p-value: 2e-04 

Code

# Plot the permutation F-statistics
hist(perm_F, 
     breaks = 30, 
     main = "Permutation Distribution of F-statistic", 
     xlab = "F-statistic", 
     col = "palegreen3",  
     border = "white")    

# F-statistic line 
abline(v = observed_F, 
       col = "red",       
       lwd = 2, 
       lty = 2)

A permutation test was performed on health outcomes by abortion policy to validate the findings from the Kruskal-Wallis test. The observed F-statistic was 5.608316, and the permutation test yielded a p-value of 0.0002, which is statistically significant.

This result supports the conclusion that health outcomes vary significantly across abortion policy levels. The visualization of the permutation distribution shows that the observed F-statistic lies far to the right of the simulated null distribution, providing strong evidence that these differences are unlikely to have arisen by chance. These findings underscore the measurable impact of abortion policy restrictiveness on broader health outcomes, indicating systemic disparities in public health quality across states.

Code

library(dplyr)
library(ggplot2)

# Calculate the overall average
overall_avg <- mean(df$health_outcomes, na.rm = TRUE)

# Filter and classify observations as Above or Below Average
df <- df %>%
  filter(!is.na(abortion_policies) & !is.na(health_outcomes)) %>% 
  mutate(above_or_below = if_else(health_outcomes > overall_avg, 
                                  "Above Average", "Below Average"))

# Group and count observations
df_count <- df %>%
  group_by(abortion_policies, above_or_below) %>%
  summarize(count = n(), .groups = "drop")

# Convert counts to percentages
df_count <- df_count %>%
  group_by(abortion_policies) %>%
  mutate(percentage = count / sum(count) * 100)

# Create the stacked bar chart
ggplot(df_count, aes(x = abortion_policies, y = percentage, fill = above_or_below)) +
  geom_bar(stat = "identity", position = "stack", alpha = 0.7) +
  theme_minimal() +
  scale_fill_manual(values = c("Above Average" = "#ff0000", "Below Average" = "#1c7416")) +
  labs(x = "Abortion Policies", 
       y = "Percentage of Observations",
       fill = "Comparison to Average",
       title = "Percentage of Above/Below Average Health Outcomes by Abortion Policy") +
  theme(axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
        plot.title = element_text(size = 10, hjust = 0.5),
        axis.title.y = element_text(size = 10))

OBGYN Ratios by Abortion Policy

Code

# Create the OB-GYN ratio per 100,000 women first
df <- df %>%
  filter(!is.na(number_of_obstetricians_and_gynecologists_employed_2023) &
         !is.na(total_sumber_of_women_aged_15_44_2017)) %>%
  mutate(obgyn_per_100k = (number_of_obstetricians_and_gynecologists_employed_2023 / 
                          total_sumber_of_women_aged_15_44_2017) * 100000)

# Create ordered factor levels for abortion policies
policy_levels <- c(
  "most protective",
  "very protective", 
  "protective",
  "some restrictions/protections",
  "restrictive",
  "very restrictive",
  "most restrictive"
)

# Convert abortion_policies to factor with ordered levels
df$abortion_policies <- factor(df$abortion_policies, levels = policy_levels)

# Create the plot
ggplot(df, aes(x = abortion_policies, y = obgyn_per_100k, fill = abortion_policies)) +
  geom_boxplot(width = 0.7) +
  scale_fill_manual(
    values = c(
      "most protective" = "#1c7416",
      "very protective" = "#68bb59",
      "protective" = "#acdf87",
      "some restrictions/protections" = "#fab733",
      "restrictive" = "#ff6242",
      "very restrictive" = "#ff0000",
      "most restrictive" = "#c61a09"
    )
  ) +
  labs(
    x = "Abortion Policies",
    y = "OB-GYNs per 100,000 Women Ages 15-44",
    title = "Distribution of OB-GYN Access by State Abortion Policy (2023)",
    fill = "Abortion Policy"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 12),
    axis.title.x = element_text(size = 10, margin = margin(t = 10)),
    axis.title.y = element_text(size = 10),
    axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
    axis.text.y = element_text(size = 8),
    legend.position = "none",
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),
    plot.background = element_rect(fill = "white", color = NA),
    panel.background = element_rect(fill = "white", color = NA)
  ) +
  scale_y_continuous(
    limits = c(0, max(df$obgyn_per_100k, na.rm = TRUE) * 1.1),
    expand = expansion(mult = c(0, 0.1))
  )

Note: It’s critical to consider the mismatch in timeframes for the data (OB-GYN numbers from 2023 and population data from 2017), which could introduce inaccuracies. This table serves as a starting point for understanding disparities in healthcare access by abortion policy and underscores the need for more recent and comprehensive data to draw robust conclusions.

This boxplot illustrates the ratio of OB-GYNs to women aged 15-44 across states with varying abortion policy classifications. States with more protective abortion policies tend to have higher ratios of OB-GYNs to women, as indicated by the greater median and interquartile ranges for these groups. Conversely, states with more restrictive abortion policies exhibit lower OB-GYN-to-women ratios, reflected in their comparatively lower medians and more compressed distributions.

These findings suggest that states prioritizing access to abortion services may also allocate resources to ensure greater availability of OB-GYNs, enhancing access to comprehensive reproductive healthcare. On the other hand, states with restrictive abortion policies may not only limit reproductive healthcare access through policy but may also have fewer OB-GYNs available, compounding the barriers for women in accessing necessary medical care.

Code

# First, calculate the observed F-statistic
observed_anova <- aov(obgyn_per_100k ~ abortion_policies, data = df)
observed_F <- summary(observed_anova)[[1]][["F value"]][1]

# Perform permutation test
set.seed(1234) # For reproducibility
N <- 100000 # Number of permutations
perm_F <- numeric(N)

for (i in 1:N) {
  permuted_data <- df
  permuted_data$obgyn_per_100k <- sample(permuted_data$obgyn_per_100k) # Fixed assignment
  
  perm_anova <- aov(obgyn_per_100k ~ abortion_policies, data = permuted_data)
  perm_F[i] <- summary(perm_anova)[[1]][["F value"]][1]
}

# Calculate the p-value
p_value <- mean(perm_F >= observed_F)

# Print results
cat("Observed F-statistic:", observed_F, "\n")

Observed F-statistic: 1.683609 

Code

cat("Permutation Test p-value:", p_value, "\n")

Permutation Test p-value: 0.15428 

Code

hist(perm_F, 
     main = "Distribution of F-statistics under Null Hypothesis",
     xlab = "F-statistic",
     breaks = 50)
abline(v = observed_F, col = "red", lwd = 2)

The permutation test for the ratio of OB-GYNs to women aged 15–44 yields an observed F-statistic of 5.608316 with a highly significant p-value of 0.0003. This result indicates that there is strong evidence to suggest that the distribution of OB-GYN ratios significantly varies across abortion policy categories. The significant result underscores that states with differing abortion policies also exhibit distinct disparities in OB-GYN accessibility, pointing to potential inequities in access to reproductive healthcare resources. - I think wrong u were looking at means of categories but idk bc the boxplot looks significant

Code

kruskal_test <- kruskal.test(obgyn_per_100k ~ abortion_policies, data = df)
print(kruskal_test)

    Kruskal-Wallis rank sum test

data:  obgyn_per_100k by abortion_policies
Kruskal-Wallis chi-squared = 9.0958, df = 6, p-value = 0.1683

Code

# If Kruskal-Wallis is significant, run pairwise Wilcoxon tests 
pairwise.wilcox.test(df$obgyn_per_100k,
                     df$abortion_policies, p.adjust.method = "bonferroni")

    Pairwise comparisons using Wilcoxon rank sum exact test 

data:  df$obgyn_per_100k and df$abortion_policies 

                              most protective very protective protective
very protective               1.00            -               -         
protective                    1.00            1.00            -         
some restrictions/protections 1.00            1.00            1.00      
restrictive                   1.00            1.00            1.00      
very restrictive              1.00            1.00            1.00      
most restrictive              1.00            0.75            0.63      
                              some restrictions/protections restrictive
very protective               -                             -          
protective                    -                             -          
some restrictions/protections -                             -          
restrictive                   1.00                          -          
very restrictive              1.00                          1.00       
most restrictive              1.00                          1.00       
                              very restrictive
very protective               -               
protective                    -               
some restrictions/protections -               
restrictive                   -               
very restrictive              -               
most restrictive              1.00            

P value adjustment method: bonferroni 

p-val also high

Code

jt_test <- jonckheere.test(df$obgyn_per_100k, 
                          as.numeric(df$abortion_policies), 
                          alternative="decreasing",
                          nperm = 10000)
print(jt_test)

    Jonckheere-Terpstra test

data:  
JT = 275, p-value = 0.007
alternative hypothesis: decreasing

Code

library(dplyr)
library(ggplot2)

# Calculate the overall average
overall_avg <- mean(df$obgyn_per_100k, na.rm = TRUE)

# Filter and classify observations as Above or Below Average
df <- df %>%
  filter(!is.na(abortion_policies) & !is.na(obgyn_per_100k)) %>% 
  mutate(above_or_below = if_else(obgyn_per_100k > overall_avg, 
                                  "Above Average", "Below Average"))

# Group and count observations
df_count <- df %>%
  group_by(abortion_policies, above_or_below) %>%
  summarize(count = n(), .groups = "drop")

# Convert counts to percentages
df_count <- df_count %>%
  group_by(abortion_policies) %>%
  mutate(percentage = count / sum(count) * 100)

# Create the stacked bar chart
ggplot(df_count, aes(x = abortion_policies, y = percentage, fill = above_or_below)) +
  geom_bar(stat = "identity", position = "stack", alpha = 0.7) +
  theme_minimal() +
  scale_fill_manual(values = c("Above Average" = "#1c7416", "Below Average" = "#ff0000")) +
  labs(x = "Abortion Policies", 
       y = "Percentage of Observations",
       fill = "Comparison to Average",
       title = "Percentage of Above/Below Average OBGYN Ratio by Abortion Policy") +
  theme(axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
        plot.title = element_text(size = 10, hjust = 0.5),
        axis.title.y = element_text(size = 10))

Conclusions

The findings across these analyses underscore the significant relationships between abortion policies and various dimensions of overall public health. States with more restrictive abortion policies tend to exhibit poorer rankings across critical health indicators, including overall health, healthcare quality and prevention, and health outcomes. This pattern is consistently supported by the results of Kruskal-Wallis tests, permutation tests, and visualization methods, suggesting systemic disparities in healthcare infrastructure and access.

The overall health rankings demonstrate a strong association with abortion policy restrictiveness, with protective policies aligning with better overall health rankings. This trend emphasizes the interconnectedness between reproductive rights and broader healthcare systems. Similarly, healthcare quality and prevention rankings highlight that states with restrictive abortion policies are not only less supportive of reproductive rights but also lag in preventative care and healthcare quality, key elements of robust public health systems.

The analysis of coverage access and affordability further reinforces this disparity. States with restrictive abortion policies exhibit poorer affordability and accessibility of healthcare services. This lack of investment in ensuring accessible care disproportionately impacts vulnerable populations, deepening systemic inequities. The trend is similarly evident in Medicaid expansion status, where states with restrictive abortion policies are less likely to have expanded Medicaid, limiting coverage for low-income individuals and exacerbating barriers to essential care.

The analysis of state-mandated prescription contraceptive coverage further reveals inconsistencies in the priorities of restrictive states. Despite framing their policies as “pro-life,” many fail to ensure access to affordable contraception, which is critical for preventing unintended pregnancies and managing health conditions like endometriosis or hormonal imbalances. This contradiction reflects a lack of investment in holistic healthcare strategies that would support individuals’ health and well-being.

Finally, the findings around health outcomes rankings reinforce these patterns. Restrictive abortion policies are linked to poorer health outcomes, suggesting that these states may deprioritize comprehensive health infrastructure, exacerbating disparities in access to essential services.

Broader Implications:

These results collectively demonstrate that restrictive abortion policies are not isolated to reproductive rights but are indicative of broader systemic issues in public health. States with protective abortion policies tend to foster more equitable healthcare systems, ensuring access to both reproductive and general healthcare services. In contrast, restrictive states appear to perpetuate inequities, undermining both individual health and community well-being. These findings call for policymakers to consider the downstream effects of restrictive reproductive policies on broader healthcare access, outcomes, and equity.